3D Data Acquisition and Registration using Two Opposing Kinects

Vahid Soleimani; Majid Mirmehdi; Dima Aldamen; Sion Hannuna; Massimo Camplani

doi:10.1109/3DV.2016.21

3D Data Acquisition and Registration using Two Opposing Kinects

Vahid Soleimani, Majid Mirmehdi, Dima Aldamen, Sion Hannuna, Massimo Camplani

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

6 Citations (Scopus)

445 Downloads (Pure)

Abstract

We present an automatic, open source data acquisition and calibration approach using two opposing RGBD sensors (Kinect V2) and demonstrate its efficacy for dynamic object reconstruction in the context of monitoring for remote lung function assessment. First, the relative pose of the two RGBD sensors is estimated through a calibration stage and rigid transformation parameters are computed.
These are then used to align and register point clouds obtained from the sensors at frame level. We validated the proposed system by performing experiments on known-size box objects with the results demonstrating accurate measurements. We also report on dynamic object reconstruction by way of human subjects undergoing respiratory functional assessment.

Original language	English
Title of host publication	2016 International Conference on 3D Vision (3DV 2016)
Subtitle of host publication	Conference Proceeding 25-28 October 2016, Stanford, CA, USA
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	128-137
Number of pages	10
ISBN (Electronic)	9781509054084
ISBN (Print)	9781509054077
DOIs	https://doi.org/10.1109/3DV.2016.21
Publication status	Published - 28 Oct 2016
Event	2016 International Conference on 3D Vision - University of Stanford, California, United States Duration: 25 Oct 2016 → 28 Oct 2016 http://3dv.stanford.edu/index.html

Conference

Conference	2016 International Conference on 3D Vision
Abbreviated title	3D Vision 2016
Country	United States
City	California
Period	25/10/16 → 28/10/16
Internet address	http://3dv.stanford.edu/index.html

Structured keywords

Digital Health

Keywords

Digital Health

Access to Document

10.1109/3DV.2016.21

Full-text PDF (accepted author manuscript)
This is the author accepted manuscript (AAM). The final published version (version of record) is available online via IEEE at DOI: 10.1109/3DV.2016.21. Please refer to any applicable terms of use of the publisher.
Accepted author manuscript, 5.2 MB

Source code page

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Projects

1 Finished

SPHERE (EPSRC IRC)

Craddock, I. J., Coyle, D. T., Flach, P. A., Kaleshi, D., Mirmehdi, M., Piechocki, R. J., Stark, B. H., Ascione, R., Ashburn, A. M., Burnett, M. E., Aldamen, D., Gooberman-Hill, R. J. S., Harwin, W. S., Hilton, G., Holderbaum, W., Holley, A. P., Manchester, V. A., Meller, B. J., Stack, E. & Gilchrist, I. D.

1/10/13 → 30/09/18

Project: Research, Parent

Cite this

Soleimani, V., Mirmehdi, M., Aldamen, D., Hannuna, S., & Camplani, M. (2016). 3D Data Acquisition and Registration using Two Opposing Kinects. In 2016 International Conference on 3D Vision (3DV 2016): Conference Proceeding 25-28 October 2016, Stanford, CA, USA (pp. 128-137). Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/3DV.2016.21

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abstract = "We present an automatic, open source data acquisition and calibration approach using two opposing RGBD sensors (Kinect V2) and demonstrate its efficacy for dynamic object reconstruction in the context of monitoring for remote lung function assessment. First, the relative pose of the two RGBD sensors is estimated through a calibration stage and rigid transformation parameters are computed.These are then used to align and register point clouds obtained from the sensors at frame level. We validated the proposed system by performing experiments on known-size box objects with the results demonstrating accurate measurements. We also report on dynamic object reconstruction by way of human subjects undergoing respiratory functional assessment.",

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author = "Vahid Soleimani and Majid Mirmehdi and Dima Aldamen and Sion Hannuna and Massimo Camplani",

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Soleimani, V, Mirmehdi, M , Aldamen, D , Hannuna, S & Camplani, M 2016, 3D Data Acquisition and Registration using Two Opposing Kinects. in 2016 International Conference on 3D Vision (3DV 2016): Conference Proceeding 25-28 October 2016, Stanford, CA, USA. Institute of Electrical and Electronics Engineers (IEEE), pp. 128-137, 2016 International Conference on 3D Vision, California, United States, 25/10/16. https://doi.org/10.1109/3DV.2016.21

3D Data Acquisition and Registration using Two Opposing Kinects. / Soleimani, Vahid; Mirmehdi, Majid ; Aldamen, Dima ; Hannuna, Sion; Camplani, Massimo.

2016 International Conference on 3D Vision (3DV 2016): Conference Proceeding 25-28 October 2016, Stanford, CA, USA. Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 128-137.

Research output: Chapter in Book/Report/Conference proceeding › Conference Contribution (Conference Proceeding)

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T1 - 3D Data Acquisition and Registration using Two Opposing Kinects

AU - Soleimani, Vahid

AU - Mirmehdi, Majid

AU - Aldamen, Dima

AU - Hannuna, Sion

AU - Camplani, Massimo

PY - 2016/10/28

Y1 - 2016/10/28

N2 - We present an automatic, open source data acquisition and calibration approach using two opposing RGBD sensors (Kinect V2) and demonstrate its efficacy for dynamic object reconstruction in the context of monitoring for remote lung function assessment. First, the relative pose of the two RGBD sensors is estimated through a calibration stage and rigid transformation parameters are computed.These are then used to align and register point clouds obtained from the sensors at frame level. We validated the proposed system by performing experiments on known-size box objects with the results demonstrating accurate measurements. We also report on dynamic object reconstruction by way of human subjects undergoing respiratory functional assessment.

AB - We present an automatic, open source data acquisition and calibration approach using two opposing RGBD sensors (Kinect V2) and demonstrate its efficacy for dynamic object reconstruction in the context of monitoring for remote lung function assessment. First, the relative pose of the two RGBD sensors is estimated through a calibration stage and rigid transformation parameters are computed.These are then used to align and register point clouds obtained from the sensors at frame level. We validated the proposed system by performing experiments on known-size box objects with the results demonstrating accurate measurements. We also report on dynamic object reconstruction by way of human subjects undergoing respiratory functional assessment.

KW - Digital Health

UR - https://github.com/BristolVisualPFT/3D_Data_Acquisition_Registration_Using_Kinects/tree/master/Double_opposing_Kinects

U2 - 10.1109/3DV.2016.21

DO - 10.1109/3DV.2016.21

M3 - Conference Contribution (Conference Proceeding)

SN - 9781509054077

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EP - 137

BT - 2016 International Conference on 3D Vision (3DV 2016)

PB - Institute of Electrical and Electronics Engineers (IEEE)

T2 - 2016 International Conference on 3D Vision

Y2 - 25 October 2016 through 28 October 2016

ER -